Author

Heeyoung Lee

Research Focus

My research examines population health, criminology, and life-course analysis with an emphasis on spatial methods. I investigate how social determinants shape health outcomes and examine patterns of structural inequality across diverse populations.

Quantitative Methods

Advanced statistical techniques using R, STATA, and specialized research tools

Spatial Analysis

Geospatial analysis using ArcGIS and GeoDa to identify patterns of inequality

Longitudinal Research

Time-series analysis to track changes in social determinants and outcomes

Project 1: Spatial and Temporal Dimension of Black-White Mental Health Paradox

This research investigates the paradoxical finding that Black Americans report better mental health outcomes than White Americans despite experiencing greater social disadvantage. Using county-level data from 2010-2020, I analyze how neighborhood context affects this relationship.

Code 
# Create simulated data
years <- 2010:2020
set.seed(123)

# Create data frame
mental_health_data <- data.frame(
  Year = rep(years, 4),
  Group = rep(c("Black Americans in High-Disadvantage Areas",
                "Black Americans in Low-Disadvantage Areas",
                "White Americans in High-Disadvantage Areas",
                "White Americans in Low-Disadvantage Areas"), each = length(years)),
  Depression_Rate = c(
    # Black Americans in High-Disadvantage Areas
    8.2 + cumsum(rnorm(length(years), 0.5, 0.2)),
    # Black Americans in Low-Disadvantage Areas
    7.8 + cumsum(rnorm(length(years), 0.2, 0.1)),
    # White Americans in High-Disadvantage Areas
    9.5 + cumsum(rnorm(length(years), 0.7, 0.2)),
    # White Americans in Low-Disadvantage Areas
    7.5 + cumsum(rnorm(length(years), 0.3, 0.1))
  )
)

# Create the interactive plot
p <- mental_health_data %>%
  ggplot(aes(x = Year, y = Depression_Rate, color = Group, group = Group)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Set1") +
  theme_minimal(base_size = 14) +
  labs(
    title = "Depression Rates by Race and Neighborhood Disadvantage",
    subtitle = "Longitudinal Trends 2010-2020",
    y = "Depression Rate (%)",
    caption = "Data source: County Health Rankings & Roadmaps, 2010-2020"
  ) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  )

# Convert to plotly for interactivity
ggplotly(p, tooltip = c("Year", "Depression_Rate", "Group")) %>%
  layout(legend = list(orientation = "h", y = -0.2))

Key Finding

73%

of counties with high racial segregation showed significant differences in mental health outcomes that cannot be explained by individual factors alone

Through spatial regression models, I demonstrate that neighborhood disadvantage has a differential impact on mental health outcomes based on racial identity, suggesting that social and community factors may buffer or exacerbate mental health stressors.

Code 
# Create a leaflet map showing a simulated county-level visualization
# This would typically use actual geographic data

# Set up a basic map of the US
map <- leaflet() %>%
  setView(lng = -98.5795, lat = 39.8283, zoom = 4) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addLegend(
    position = "bottomright",
    colors = colorRampPalette(c("#f7fbff", "#08519c"))(5),
    labels = c("Very Low", "Low", "Moderate", "High", "Very High"),
    title = "Mental Health Disparity Index",
    opacity = 0.7
  )

# Display the map
map

“The temporal dimension of neighborhood effects is crucial to understanding mental health disparities. Our findings suggest that cumulative exposure to disadvantage over time, rather than point-in-time measurements, better explains the complex relationship between race, place, and mental health.”

Project 2: Exposure to Police Killings and Adolescents’ Self-Concept

This study examines how exposure to police killings in local communities affects adolescents’ self-concept and development. Using longitudinal data from the National Longitudinal Study of Adolescent to Adult Health combined with geospatial data on police killings, I explore racial differences in psychological responses to these events.

Code 
# Create simulated data for psychological measures
measures <- c("Self-Esteem", "Depression", "Social Anxiety", "Future Outlook", "Sense of Safety")
groups <- c("Black Youth (Exposed)", "Black Youth (Not Exposed)",
            "White Youth (Exposed)", "White Youth (Not Exposed)")

# Create effect sizes with realistic patterns
effects <- data.frame(
  Measure = rep(measures, each = 4),
  Group = rep(groups, times = 5),
  Effect = c(
    # Self-Esteem (negative values = decrease)
    -0.42, -0.08, -0.15, -0.03,
    # Depression (positive values = increase)
    0.38, 0.11, 0.17, 0.05,
    # Social Anxiety
    0.29, 0.13, 0.14, 0.06,
    # Future Outlook
    -0.35, -0.12, -0.12, -0.05,
    # Sense of Safety
    -0.48, -0.15, -0.21, -0.07
  )
)

# Set factor levels for correct ordering
effects$Measure <- factor(effects$Measure, levels = measures)
effects$Group <- factor(effects$Group, levels = groups)

# Create the plot
p2 <- ggplot(effects, aes(x = Measure, y = Effect, fill = Group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
  scale_fill_manual(values = c("#9966FF", "#C8B6E2", "#4ECDC4", "#A6E1DB")) +
  coord_flip() +
  theme_minimal(base_size = 14) +
  labs(
    title = "Impact of Exposure to Police Killings on Psychological Measures",
    subtitle = "Standardized Effect Sizes by Race and Exposure Status",
    y = "Standardized Effect Size",
    x = "",
    caption = "Data source: National Longitudinal Study of Adolescent to Adult Health, 2018-2022"
  ) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  )

# Convert to plotly for interactivity
ggplotly(p2, tooltip = c("y", "x", "fill")) %>%
  layout(legend = list(orientation = "h", y = -0.2))

Timeline of Youth Response to Police Killings

<div class="timeline-content">
  <h5>Proximity Analysis</h5>
  <p>Youth living within 5-mile radius of police killings showed measurable changes in psychological well-being</p>
</div>
<div class="timeline-content">
  <h5>Response Differences</h5>
  <p>Black youth demonstrated significant declines in self-esteem and increases in self-reported depression</p>
</div>
<div class="timeline-content">
  <h5>Temporal Effects</h5>
  <p>Effects persisted for up to 12 months following incidents, suggesting long-term impacts</p>
</div>

Project 3: Unequal Spillover of Human Capital

This research examines how the presence of college-educated populations within counties affects mortality rates among both college-educated and non-college-educated residents. I hypothesize that human capital spillover effects create health benefits that are unevenly distributed.

Code 
# Create simulated data for mortality rates
years <- seq(2010, 2022, by = 2)

# Create data frame
mortality_data <- data.frame(
  Year = rep(years, 4),
  Group = rep(c("College Degree - High Education County",
                "College Degree - Low Education County",
                "No College Degree - High Education County", 
                "No College Degree - Low Education County"), each = length(years)),
  Mortality_Rate = c(
    # College Degree - High Education County
    c(510, 498, 485, 473, 465, 480, 462),
    # College Degree - Low Education County
    c(545, 538, 530, 525, 518, 535, 522),
    # No College Degree - High Education County
    c(725, 735, 745, 752, 760, 790, 772),
    # No College Degree - Low Education County
    c(780, 795, 805, 820, 835, 865, 848)
  )
)

# Create the interactive plot
p3 <- mortality_data %>%
  ggplot(aes(x = Year, y = Mortality_Rate, color = Group, group = Group)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Set2") +
  theme_minimal(base_size = 14) +
  labs(
    title = "Mortality Rates by Educational Attainment and County Education Level",
    subtitle = "Longitudinal Trends 2010-2022",
    y = "Age-Adjusted Mortality Rate (per 100,000)",
    caption = "Data source: CDC Wonder Database & American Community Survey, 2010-2022"
  ) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  )

# Convert to plotly for interactivity
ggplotly(p3, tooltip = c("Year", "Mortality_Rate", "Group")) %>%
  layout(legend = list(orientation = "h", y = -0.2))

Key Finding

58%

higher reduction in mortality rates among college-educated populations in counties with the highest percentage of college graduates

Using spatial analysis techniques, I’ve mapped mortality differentials across U.S. counties:

Code 
# Create a leaflet map showing a simulated county-level visualization of mortality rates
# This would use actual geographic data in a real application

map2 <- leaflet() %>%
  setView(lng = -98.5795, lat = 39.8283, zoom = 4) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addLegend(
    position = "bottomright",
    colors = colorRampPalette(c("#ffffcc", "#a1dab4", "#41b6c4", "#2c7fb8", "#253494"))(5),
    labels = c("Very Low", "Low", "Moderate", "High", "Very High"),
    title = "Mortality Differential Index",
    opacity = 0.7
  )

# Display the map
map2

Methodology Techniques

Spatial Autocorrelation

Used Moran’s I to identify clustering of mortality rates and college education across counties

Hierarchical Linear Modeling

Accounted for nested effects of individuals within counties and counties within states

Difference-in-Differences

Measured changes in mortality differentials before and after policy interventions

Publication List & Ongoing Projects

Below is a searchable table of my publications related to these research themes:

Code 
# Create sample publication data
publications <- data.frame(
  Year = c(2025, 2025, 2025, 2024, 2024, 2024, 2024, 2024, 2023, 2023),
  Title = c(
    "Unequal spillover of human capital: longitudinal analysis of mortality among populations by college degree in US counties, 2010-2022",
    "Features of Residential Counties and Individual Health Conditions for Opioid Use Disorder among Older Medicare Beneficiaries",
    "Exposure to Police Killings and Adolescents' Self-Concept: Diverging Impact for Black and White American Youth",
    "Spatial and Temporal Dimension of Black-White Mental Health Paradox in the US: Effect of Cumulative Neighborhood Disadvantage",
    "State-Level Structural Sexism and Adolescent Sexual Violence Victimization in the US",
    "Delinquency During Puberty as a Biosocial Behavior",
    "Moving Apart and Coming Together",
    "Gender Differences in the Relationship between Coming Out as LGB to Family and Depression in South Korea",
    "Industry Culture Matters: Sexual Harassment in the South Korean Film Industry",
    "Open, Discreet, and Repressive Closet: Latent Class Analysis on the Patterns of LGBTQ+ Youth's Coming Out"
  ),
  Journal = c(
    "American Journal of Preventive Medicine",
    "The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences",
    "Journal of Adolescence",
    "Population Research & Policy Review",
    "Child Abuse and Neglect",
    "Criminal Justice and Behaviors",
    "Journal of Homosexuality",
    "Society and Mental Health",
    "Violence Against Women",
    "The Korean Journal of Sociology"
  ),
  Method = c(
    "Spatial Analysis, Longitudinal", 
    "Multilevel Modeling",
    "Difference-in-Differences",
    "Spatial Regression",
    "Hierarchical Linear Modeling",
    "Time-Varying Effects Model",
    "Network Analysis",
    "Latent Class Analysis",
    "Qualitative Analysis",
    "Latent Class Analysis"
  )
)

# Create the interactive datatable
DT::datatable(
  publications,
  options = list(
    pageLength = 5,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf'),
    lengthMenu = list(c(5, 10, -1), c('5', '10', 'All'))
  ),
  rownames = FALSE,
  filter = 'top',
  class = 'cell-border stripe',
  caption = htmltools::tags$caption(
    style = 'caption-side: bottom; text-align: center;',
    'Table 1: ', htmltools::em('Publications related to research projects')
  )
)

Ongoing Research

I am currently working on several projects that extend these research areas:

  1. Change in Cognitive Function after Marriage Dissolution in the U.S.
    • Using quasi-experimental design to examine causal relationships
    • Analyzing data from the Health and Retirement Study
  2. One Foot in, One Foot Out: Spatial Spillover Effects of State Policy Liberalism and Mortality Rate in the U.S.
    • Examining how policy environments affect health outcomes across state borders
    • Using spatial lag models to identify spillover effects
  3. Contested Boundary and Racial Threat
    • Collaboration with Matt Vogel and Theodore Lentz
    • Examining how demographic changes influence perceptions of safety and policing

If you’re interested in collaborating on any of these topics, please contact me.